Not sure either... unless it means a workflow where instead of chunking your knowledgebase docs and generating embeddings, you do entity and relationship extractions on them instead and store an index of knowledge graph serialisations to source documents.

You can do set overlap on the extracted terms and that gives similar relevancy to the chunks as done by their embedding vectors. It’s just a lower dimensionally of the relationships between the chunk and the query.